‘The benefits do not reach us’: analyzing the discrepancies between the state recognition of hijra and their reality in Dhaka, Bangladesh
Bibliographic record
Abstract
Hijra, a transgender group in Bangladesh, despite being acknowledged by the government in 2013 as a separate gender category, cannot adequately exercise their gender and sexual rights. This study aimed to explore discrepancies between the gender declaration and their lived realities of their gender and sexual rights. This study adopted the policy analysis framework, whilst linking it to SRHR frameworks by the Guttmacher-Lancet Commission. This study adopted desk review and mixed methods research to explore their ability to exercise their sexual and reproductive health rights as hijra. A total of 298 hijra participated in the study and completed quantitative surveys. Among them, 20 mutually exclusive groups of participants also completed 20 in-depth interviews and five focus groups (of 4-5 participants, totaling 20-25 participants). Data were analyzed through descriptive statistics and thematic analysis. The findings indicated that 79.2% obfuscated their hijra identity, 89.8% of whom hid from their family. Of the participants, 80.9% hid their partners due to fear of stigma and 82.2% reported societal discrimination. Almost all participants (98.7%) reported gender-based discrimination. The qualitative findings revealed motifs of exclusion and forced duplicity emerged where hijra disguised their identities, denied services, and faced gender-based discrimination in various settings including healthcare, education and employment. Legal recognition is a crucial step in improving health and quality of life for hijra, however, much work remains to advance their SRHR such as advocacy, cultural competency training of institutional service providers, and community mobilization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".